A hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO) is proposed, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.
Abstract
Low-altitude urban environments pose significant challenges to multi-UAV trajectory planning because of dense buildings, constrained airspace, dynamic obstacles, inter-UAV conflicts, and terminal-area congestion. This study proposes a hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization (ACO) with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO). Environmental costs and predicted dynamic-obstacle risks are incorporated into the ACO global search to generate risk-aware reference trajectories, while a sliding-window GWO improves trajectory smoothness and execution feasibility. During online execution, cooperative MPC-GWO combines dynamic-obstacle prediction, inter-UAV separation constraints, reconfigurable formation switching, and goal-neighborhood safety control to achieve adaptive obstacle avoidance, cooperative replanning, and orderly terminal arrival. Thirty-run Monte Carlo simulations show that the proposed method achieves a success rate of 93.3% ± 25.4% and the highest composite score of 96.20 ± 5.30, with zero dynamic-obstacle and inter-UAV collisions. Ablation experiments verify the effectiveness of the dynamic prediction, formation reconfiguration, and terminal safety-control mechanisms. The average online replanning time remains below 0.5 s, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.
Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N=50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60±0.18 s and a path success rate of 95.8±1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections.
Changqi Yang, Hongjie Hu, Yi Ai· Drones· 0 citations
3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA) are formed.
Gengsong Li, Yi Liu, Qibin Zheng et al.· Applied Sciences· 0 citations
A distributed real-time trajectory-planning method that integrates a distributed model predictive control framework with an adaptive Gaussian collocation strategy (DA-GCMPC) was developed, which achieves lower computation time and better trajectory quality metrics under the tested simulation settings.
Yang Zhao, Mingying Huo, Naiming Qi et al.· Drones· 0 citations
Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation.
Jian Wu, Shengchang Liu, Wenxi Ni et al.· Drones· 0 citations
Simulation results show that the proposed hierarchical task planning framework significantly outperforms traditional approaches in efficiency, robustness, and scalability, highlighting its strong potential for UAV swarm mission planning in complex environments.
Yalan Peng, Haibin Duan, Ming Li et al.· Science China Technological...· 0 citations
Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed distributed NMPC-based trajectory planning method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error.
Ying-Ting Cui, Tong-Xin Zeng, Bin Li· Drones· 0 citations
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